Rational design and adaptive management of combination therapies for Hepatitis C virus infection [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 616.79 Diseases
• Thông tin xuất bản: Los Alamos, N.M. : Oak Ridge, Tenn. : Los Alamos National Laboratory ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2015
• Mô tả vật lý: Size: e1004040 : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 261117
• Chủ đề: 1. 60 applied life sciences; 2. Cell death; 3. Dose prediction methods; 4. Drug therapy; 5. Hepatitis c virus; 6. Human genetics; 7. Number theory; 8. Simulation and modeling; 9. Treatment guidelines

Tóm tắt

 Recent discoveries of direct acting antivirals against Hepatitis C virus (HCV) have raised hopes of effective treatment via combination therapies. Yet rapid evolution and high diversity of HCV populations, combined with the reality of suboptimal treatment adherence, make drug resistance a clinical and public health concern. We develop a general model incorporating viral dynamics and pharmacokinetics/ pharmacodynamics to assess how suboptimal adherence affects resistance development and clinical outcomes. We derive design principles and adaptive treatment strategies, identifying a high-risk period when missing doses is particularly risky for <
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  resistance, and quantifying the number of additional doses needed to compensate when doses are missed. Using data from large-scale resistance assays, we demonstrate that the risk of resistance can be reduced substantially by applying these principles to a combination therapy of daclatasvir and asunaprevir. By providing a mechanistic framework to link patient characteristics to the risk of resistance, these findings show the potential of rational treatment design.